Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review

Title

Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review

Creator

Gaba I.; Ramamurthy B.

Description

This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination can not only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The Survey is hybrid with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction based on texture extraction, color extraction, and shape extraction. The Electrochemical Society

Source

ECS Transactions, Vol-107, No. 1, pp. 2053-2071.

Date

2022-01-01

Publisher

Institute of Physics

Coverage

Gaba I., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Ramamurthy B., Department of Computer Science, Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 19386737; ISBN: 978-160768539-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1149/10701.2053ecst

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